Exploiting comments information to improve legal public opinion news abstractive summarization  

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作  者:Yuxin HUANG Zhengtao YU Yan XIANG Zhiqiang YU Junjun GUO 

机构地区:[1]Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming,650500,China [2]Yunnan Key Laboratory of Artificial Intelligence,Kunming University of Science and Technology,Kunming,650500,China

出  处:《Frontiers of Computer Science》2022年第6期31-40,共10页中国计算机科学前沿(英文版)

基  金:supported by the National Key Research and Development Program of China (2018YFC0830105,2018YFC 0830101,2018YFC0830100);the National Natural Science Foundation of China (Grant Nos.61972186,61762056,61472168);the Yunnan Provincial Major Science and Technology Special Plan Projects (202002AD080001);the General Projects of Basic Research in Yunnan Province (202001AT070046,202001AT070047).

摘  要:Automatically generating a brief summary for legal-related public opinion news(LPO-news,which contains legal words or phrases)plays an important role in rapid and effective public opinion disposal.For LPO-news,the critical case elements which are significant parts of the summary may be mentioned several times in the reader comments.Consequently,we investigate the task of comment-aware abstractive text summarization for LPO-news,which can generate salient summary by learning pivotal case elements from the reader comments.In this paper,we present a hierarchical comment-aware encoder(HCAE),which contains four components:1)a traditional sequenceto-sequence framework as our baseline;2)a selective denoising module to filter the noisy of comments and distinguish the case elements;3)a merge module by coupling the source article and comments to yield comment-aware context representation;4)a recoding module to capture the interaction among the source article words conditioned on the comments.Extensive experiments are conducted on a large dataset of legal public opinion news collected from micro-blog,and results show that the proposed model outperforms several existing state-of-the-art baseline models under the ROUGE metrics.

关 键 词:legal public opinion news abstractive summarization COMMENT comment-aware context case elements bidirectional attention 

分 类 号:TP391[自动化与计算机技术—计算机应用技术]

 

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